TensorFlow 基本变量定义,基本操作,矩阵基本操作
2017-07-19 19:49
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使用 TensorFlow 进行基本操作的实例,这个实例主要是使用 TensorFlow 进行了加法运算。 包括使用 constant 常量进行加法运算和使用 placeholder 进行变量加法运算,以及扩展到矩阵的加法运算。 TensorFlow 变量定义,加法运算。 # -*- coding:utf-8 -*- from __future__ import print_function ''' 使用 TensorFlow 进行基本操作的实例,这个实例主要是使用 TensorFlow 进行了加法运算。 包括使用 constant 常量进行加法运算和使用 placeholder 进行变量加法运算,以及扩展到矩阵的加法运算。 TensorFlow 变量定义,加法运算。 ''' ''' Basic Operations example using TensorFlow library. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' import tensorflow as tf # Basic constant operations # The value returned by the constructor represents the output # of the Constant op. a = tf.constant(2) b = tf.constant(3) # Launch the default graph. with tf.Session() as sess: print("a=2, b=3") print("Addition with constants: %i" % sess.run(a+b)) print("Multiplication with constants: %i" % sess.run(a*b)) # Basic Operations with variable as graph input # The value returned by the constructor represents the output # of the Variable op. (define as input when running session) # tf Graph input a = tf.placeholder(tf.int16) b = tf.placeholder(tf.int16) # Define some operations add = tf.add(a, b) mul = tf.multiply(a, b) # Launch the default graph. with tf.Session() as sess: # Run every operation with variable input print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})) print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})) # ---------------- # More in details: # Matrix Multiplication from TensorFlow official tutorial # Create a Constant op that produces a 1x2 matrix. The op is # added as a node to the default graph. # # The value returned by the constructor represents the output # of the Constant op. matrix1 = tf.constant([[3., 3.]]) # Create another Constant that produces a 2x1 matrix. matrix2 = tf.constant([[2.],[2.]]) # Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs. # The returned value, 'product', represents the result of the matrix # multiplication. product = tf.matmul(matrix1, matrix2) # To run the matmul op we call the session 'run()' method, passing 'product' # which represents the output of the matmul op. This indicates to the call # that we want to get the output of the matmul op back. # # All inputs needed by the op are run automatically by the session. They # typically are run in parallel. # # The call 'run(product)' thus causes the execution of threes ops in the # graph: the two constants and matmul. # # The output of the op is returned in 'result' as a numpy `ndarray` object. with tf.Session() as sess: result = sess.run(product) print(result) # ==> [[ 12.]] 查看更多 TensorFlow 教程:http://www.tensorflownews.com/ |
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